Knowledge Flow: Improve Upon Your Teachers
Authors: Iou-Jen Liu, Jian Peng, Alexander Schwing
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate knowledge flow on a variety of tasks from reinforcement learning to fully-supervised learning. |
| Researcher Affiliation | Academia | University of Illinois at Urbana-Champaign |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of its source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate knowledge flow on reinforcement learning using Atari games that were used by Rusu et al. (2016b); Fernando et al. (2017). For supervised learning, we use a variety of image classification benchmarks, including CIFAR10 (Krizhevsky, 2009), CIFAR-100 (Krizhevsky, 2009), STL-10 (Coates et al., 2011), and EMNIST (Cohen et al., 2017). |
| Dataset Splits | Yes | The parameters λ1 for the dependent cost and λ2 for the KL cost are determined using the validation set of each dataset. |
| Hardware Specification | No | As A3C, we run 16 agents on 16 CPU cores in parallel. (This is not specific enough to meet the criteria for "Yes") |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for replication. |
| Experiment Setup | Yes | The learning rate is set to 10 4 and gradually decreased to zero for all experiments. To select λ1 and λ2 in our framework, we follow progressive neural net (Rusu et al., 2016b): randomly sample λ1 {0.05, 0.1, 0.5} and λ2 {0.001, 0.01, 0.05}. |